{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14c11c13-4fef-48c4-b2c6-9cb80114d7ae",
   "metadata": {},
   "source": [
    "### 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "122dee9d-bacc-474e-be0d-e0cda205a081",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from torchvision import transforms, datasets\n",
    "import os\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfc87e00-5f9e-47a0-8d44-cea4adf12b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查是否有可用的 GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "610cf08d-474d-495f-b1ee-c88bad404d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 22500\n",
       "    Root location: ../datasets/PetImages\\train\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               RandomResizedCrop(size=(150, 150), scale=(0.8, 1.0), ratio=(0.75, 1.3333), interpolation=bilinear, antialias=True)\n",
       "               ToTensor()\n",
       "           )"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这里没有用项目二中的数据增强标准化等方法, 是为了保持原图形状, 更好地进行展示\n",
    "transform = transforms.Compose([\n",
    "    transforms.RandomResizedCrop(150, scale=(0.8, 1.0)),  # 把图片的每一个channel随机裁剪成 150x150, 裁剪比例范围为 80% - 100%\n",
    "    transforms.ToTensor(),             # 将图像转换为张量，并将像素值归一化到 [0, 1]\n",
    "])\n",
    "\n",
    "\n",
    "root_dir=r\"../datasets/PetImages\"\n",
    "\n",
    "\"\"\"\n",
    "datasets.ImageFolder 用于加载图像数据集, 适合用于有文件夹层级结构的分类数据集\n",
    "它会自动根据文件夹结构将数据与标签（类别编号）配对\n",
    "对于如下的文件夹结构：\n",
    "train/\n",
    "    cats/\n",
    "        cat1.jpg\n",
    "        cat2.jpg\n",
    "    dogs/\n",
    "        dog1.jpg\n",
    "        dog2.jpg\n",
    "        \n",
    "ImageFolder 处理之后的结果为：\n",
    "[\n",
    "    (image1_tensor, 0),  # 对应 cats 类别\n",
    "    (image2_tensor, 0),  # 对应 cats 类别\n",
    "    (image3_tensor, 1),  # 对应 dogs 类别\n",
    "    (image4_tensor, 1),  # 对应 dogs 类别\n",
    "]\n",
    "\"\"\"\n",
    "# 加载训练集数据，应用 transforms\n",
    "train_data = datasets.ImageFolder(\n",
    "    os.path.join(root_dir, 'train'),  # 训练数据所在路径\n",
    "    transform                    # 数据增强和预处理\n",
    ")\n",
    "\n",
    "# 加载测试集数据，应用 transforms\n",
    "test_data = datasets.ImageFolder(\n",
    "    os.path.join(root_dir, 'test'),  # 测试数据所在路径\n",
    "    transform                    # 数据增强和预处理\n",
    ")\n",
    "\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9641338e-1811-466f-8670-748d1de65899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集类别数量: 2\n",
      "训练集类别名称: ['Cat', 'Dog']\n",
      "训练集数据数量: 22500\n",
      "训练集第一个样本: torch.Size([3, 150, 150]), 0\n",
      "测试集类别数量: 2\n",
      "测试集类别名称: ['Cat', 'Dog']\n",
      "测试集数据数量: 2500\n",
      "测试集第一个样本: torch.Size([3, 150, 150]), 0\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集的内容\n",
    "print(f\"训练集类别数量: {len(train_data.classes)}\")\n",
    "print(f\"训练集类别名称: {train_data.classes}\")\n",
    "print(f\"训练集数据数量: {len(train_data)}\")\n",
    "print(f\"训练集第一个样本: {train_data[0][0].shape}, {train_data[0][1]}\")  # 打印第一个样本 (image, label)\n",
    "\n",
    "print(f\"测试集类别数量: {len(test_data.classes)}\")\n",
    "print(f\"测试集类别名称: {test_data.classes}\")\n",
    "print(f\"测试集数据数量: {len(test_data)}\")\n",
    "print(f\"测试集第一个样本: {test_data[0][0].shape}, {test_data[0][1]}\")  # 打印第一个样本 (image, label)\n",
    "# 图片形状torch.Size([3, 150, 150] : 150, 150是图片大小， 3代表三个通道(r, g, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96624a8e-658b-4bae-b8f5-c5794f9b1666",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看数据集图片\n",
    "\n",
    "# 定义函数将 Tensor 转为 numpy 格式的图片\n",
    "def imshow(img, title):\n",
    "    img = img.numpy().transpose((1, 2, 0))  # 将通道维度移到最后 (H, W, C)\n",
    "    img = np.clip(img, 0, 1)  # 确保像素值在 [0, 1] 范围\n",
    "    plt.imshow(img)\n",
    "    plt.title(title)\n",
    "    plt.axis('off')\n",
    "\n",
    "# 获取两张不同类别的图像\n",
    "def get_two_classes_samples(dataset):\n",
    "    \"\"\"\n",
    "    高效获取数据集中每个类别的样本。\n",
    "    \"\"\"\n",
    "    class_indices = {label: None for label in range(len(dataset.classes))}  # 存储每个类别的索引\n",
    "    samples = {}  # 存储样本\n",
    "    half_len = len(dataset) // 2  # 数据集一半的长度\n",
    "    \n",
    "    # 遍历前后索引寻找每个类别的第一个样本\n",
    "    for idx in range(half_len):\n",
    "        # 从前往后找\n",
    "        _, label = dataset[idx]\n",
    "        if class_indices[label] is None:\n",
    "            class_indices[label] = idx  # 记录该类别的第一个样本索引\n",
    "            samples[label] = dataset[idx][0]  # 存储对应的图片数据\n",
    "        \n",
    "        # 从后往前找\n",
    "        _, label = dataset[len(dataset) - 1 - idx]\n",
    "        if class_indices[label] is None:\n",
    "            class_indices[label] = len(dataset) - 1 - idx\n",
    "            samples[label] = dataset[len(dataset) - 1 - idx][0]\n",
    "        \n",
    "        # 如果已找到所有类别，停止搜索\n",
    "        if len(samples) == len(dataset.classes):\n",
    "            break\n",
    "\n",
    "    return samples\n",
    "\n",
    "\n",
    "# 从训练集获取两张不同类别的图像\n",
    "class_samples = get_two_classes_samples(train_data)\n",
    "\n",
    "# 可视化\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i, (label, img) in enumerate(class_samples.items()):\n",
    "    plt.subplot(1, 2, i+1)\n",
    "    imshow(img, f\"Class: {train_data.classes[label]}\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "486d81df-7a88-426d-a2a9-a3d6dfe19088",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=32\n",
    "\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, pin_memory=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b88781-33ad-41d8-b5cc-0fbda715772c",
   "metadata": {},
   "source": [
    "### 模型构建\n",
    "\n",
    "`nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)`\n",
    "\n",
    "卷积层输出计算公式：\n",
    "$N = (W - F + 2P) / S + 1$\n",
    "其中，\n",
    "- N：输出大小\n",
    "- W：输入大小\n",
    "- F：卷积核大小\n",
    "- P：填充值大小\n",
    "- S：步长大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2ef1a4f-d87f-4e86-95ce-76add5cc4aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        # 输入形状: (batch, 3, 150, 150)\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        self.conv1 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 32, 150, 150)\n",
    "            torch.nn.BatchNorm2d(32),  # 增加 Batch Normalization 提高训练稳定性\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 32, 75, 75)\n",
    "        )\n",
    "\n",
    "        self.conv2 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 64, 75, 75)\n",
    "            torch.nn.BatchNorm2d(64),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 64, 37, 37)\n",
    "        )\n",
    "\n",
    "        self.conv3 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 128, 37, 37)\n",
    "            torch.nn.BatchNorm2d(128),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 128, 18, 18)\n",
    "        )\n",
    "        self.conv4 = torch.nn.Sequential(\n",
    "            torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),  # 输出: (batch, 256, 18, 18)\n",
    "            torch.nn.BatchNorm2d(256),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.MaxPool2d(kernel_size=2, stride=2)  # 输出: (batch, 256, 9, 9)\n",
    "        )\n",
    "\n",
    "        self.fc = torch.nn.Sequential(\n",
    "            torch.nn.Linear(256 * 9 * 9, 512),  # 全连接层, 输入: 128*18*18\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Dropout(0.5),  # 添加 Dropout 防止过拟合\n",
    "            torch.nn.Linear(512, 2)  # 最后输出 2 个类别\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size = x.size(0)\n",
    "        x = self.conv1(x)  # 第一卷积模块\n",
    "        x = self.conv2(x)  # 第二卷积模块\n",
    "        x = self.conv3(x)  # 第三卷积模块\n",
    "        x = self.conv4(x)  # 第四卷积模块\n",
    "        x = x.view(batch_size, -1)  # 展平为全连接层输入\n",
    "        x = self.fc(x)  # 全连接层\n",
    "        return x  # 输出形状: (batch, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "17534800-b9ee-4066-a956-c465117c1f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Net().to(device)\n",
    "\n",
    "lr=2e-3\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "loss_fn = torch.nn.CrossEntropyLoss()  # 适用于多（二）分类问题, 适合模型输出为 (batch_size, class_num)\n",
    "\"\"\"\n",
    "权重衰减 (L2正则化)\n",
    "在优化器中加入权重衰减，可以防止过拟合\n",
    "大的权重值会导致模型对训练数据的拟合过度，从而丧失对新数据的泛化能力。\n",
    "L2 正则化通过惩罚大权重值，迫使模型学到更平滑的特征分布，而不是依赖特定特征的绝对大小。\n",
    "\"\"\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-4) # 使用 Adam 优化器\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "\"\"\"\n",
    "学习率衰减策略\n",
    "StepLR: 每隔一定的步数衰减学习率\n",
    "它会在每 step_size 个 epoch 后将学习率乘以一个因子 gamma\n",
    "这种方法可以在模型训练进入瓶颈时，减少学习率，从而让优化器更精细地调整权重，避免震荡或错过局部最优\n",
    "\"\"\"\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c176546a-0150-4278-a70f-37e1e6d92a22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): Linear(in_features=20736, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
      "  )\n",
      ")\n",
      "模型总参数数量: 11,007,746\n",
      "模型可训练参数数量: 11,007,746\n"
     ]
    }
   ],
   "source": [
    "# 查看模型结构\n",
    "# 打印模型参数总数和可训练参数总数\n",
    "def count_parameters(model):\n",
    "    total_params = sum(p.numel() for p in model.parameters())  # 所有参数数量\n",
    "    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)  # 需要训练的参数数量\n",
    "    print(f\"模型总参数数量: {total_params:,}\")\n",
    "    print(f\"模型可训练参数数量: {trainable_params:,}\")\n",
    "\n",
    "print(model)\n",
    "count_parameters(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5850833-7228-4f04-ae20-a289b7a8c9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练函数\n",
    "def train(dataloader, model, loss_fn, optimizer):\n",
    "    model.train()  # 设置模型为训练模式\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    # 使用 tqdm 包裹数据加载器，显示进度条 (因为训练过程会比较慢)\n",
    "    progress_bar = tqdm(dataloader, desc=\"Training\", leave=False)\n",
    "    for images, labels in progress_bar:\n",
    "        # 将数据移动到设备\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        # 前向传播\n",
    "        outputs = model(images)\n",
    "        loss = loss_fn(outputs, labels)\n",
    "\n",
    "        # 反向传播和优化\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()  # 更新模型参数\n",
    "\n",
    "        # 统计指标\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "        # 更新进度条描述\n",
    "        progress_bar.set_postfix(loss=loss.item())\n",
    "        \n",
    "    scheduler.step()  # 更新学习率\n",
    "\n",
    "    accuracy = 100 * correct / total\n",
    "    avg_loss = running_loss / len(dataloader)\n",
    "    return avg_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94df5294-4bc0-43d9-8fcc-c723e35e8231",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试函数\n",
    "def evaluate(dataloader, model, loss_fn):\n",
    "    model.eval()  # 设置模型为评估模式\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "\n",
    "    with torch.no_grad():  # 关闭梯度计算\n",
    "        progress_bar = tqdm(dataloader, desc=\"Evaluating\", leave=False)\n",
    "        for images, labels in dataloader:\n",
    "            # 将数据移动到设备\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            # 前向传播\n",
    "            outputs = model(images)\n",
    "            loss = loss_fn(outputs, labels)\n",
    "\n",
    "            # 统计指标\n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "            # 更新进度条描述\n",
    "            progress_bar.set_postfix(loss=loss.item())\n",
    "\n",
    "    accuracy = 100 * correct / total\n",
    "    avg_loss = running_loss / len(dataloader)\n",
    "    return avg_loss, accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776bace7-71c5-4b94-b098-c46f031de9ca",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43f96cab-f89b-4391-987d-54a520ed83a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  86%|██████████████████████████████████████████████████        | 607/704 [03:23<00:35,  2.76it/s, loss=0.537]D:\\develop\\anaconda\\envs\\pytorch\\lib\\site-packages\\PIL\\TiffImagePlugin.py:900: UserWarning: Truncated File Read\n",
      "  warnings.warn(str(msg))\n",
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Train_acc:71.2%, Train_loss:0.560, Test_acc:77.3%，Test_loss:0.479\n",
      "Epoch 2/3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2, Train_acc:79.7%, Train_loss:0.437, Test_acc:82.7%，Test_loss:0.392\n",
      "Epoch 3/3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3, Train_acc:83.4%, Train_loss:0.379, Test_acc:83.2%，Test_loss:0.375\n",
      "训练完成!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "\"\"\"\n",
    "如果不想训练太久可以适当减小epoch\n",
    "epoch=10的时候训练正确率就达到了77.0%, epoch=30时才为81.0% \n",
    "再增加epoch训练效果增加不明显, 但是结果明显还未收敛。\n",
    "为了更好的效果作者就增大了epoch\n",
    "\"\"\"\n",
    "num_epochs = 3\n",
    "\n",
    "train_loss = []\n",
    "train_acc  = []\n",
    "test_loss  = []\n",
    "test_acc   = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "    \n",
    "    epoch_train_loss, epoch_train_acc = train(train_loader, model, loss_fn, optimizer)\n",
    "\n",
    "    # 在测试集上评估\n",
    "    epoch_test_loss, epoch_test_acc = evaluate(test_loader, model, loss_fn)\n",
    "\n",
    "    train_acc.append(epoch_train_acc)\n",
    "    train_loss.append(epoch_train_loss)\n",
    "    test_acc.append(epoch_test_acc)\n",
    "    test_loss.append(epoch_test_loss)\n",
    "    # 打印训练和测试结果\n",
    "    template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}')\n",
    "    print(template.format(epoch+1, epoch_train_acc, epoch_train_loss, epoch_test_acc, epoch_test_loss))\n",
    "\n",
    "print(\"训练完成!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5565e316-1967-41a0-810c-aa17f2809ed9",
   "metadata": {},
   "source": [
    "### 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca6ed2f2-317a-4118-8707-16ecd7b2f5f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs_range = range(num_epochs)\n",
    "\n",
    "plt.figure(figsize=(12, 3))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "plt.plot(epochs_range, train_acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, test_acc, label='Test Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, train_loss, label='Training Loss')\n",
    "plt.plot(epochs_range, test_loss, label='Test Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34c64fa-9e5d-4bd2-a4ce-59630b82f5e7",
   "metadata": {},
   "source": [
    "### 模型保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69db0aa7-3203-4d76-a84e-2a36844867d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指定保存路径\n",
    "save_dir = '../models/6_Adversarial_Attack'\n",
    "\n",
    "# 确保目录存在，如果不存在则创建\n",
    "import os\n",
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "\n",
    "# 保存模型\n",
    "torch.save(model.state_dict(), os.path.join(save_dir, 'model_weights.pth'))\n",
    "\n",
    "# # 加载模型参数\n",
    "# model.load(torch.load(os.path.join(save_dir, 'model_weights.pth')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f1bd51d-6ef7-4f6c-8552-e475b168e544",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
